dann代码pytorch

以下是使用PyTorch实现的ChitGPT模型代码，其中包括了模型的定义和训练过程。

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

class ChitGPT(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers):
        super(ChitGPT, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_dim, vocab_size)

    def forward(self, x, hidden):
        embedded = self.embedding(x)
        output, hidden = self.lstm(embedded, hidden)
        output = self.fc(output)
        return output, hidden

    def init_hidden(self, batch_size):
        weight = next(self.parameters()).data
        return (weight.new(self.num_layers, batch_size, self.hidden_dim).zero_(),
                weight.new(self.num_layers, batch_size, self.hidden_dim).zero_())

class ChitGPTDataset(Dataset):
    def __init__(self, text, seq_length, device):
        self.seq_length = seq_length
        self.device = device
        self.vocab = sorted(list(set(text)))
        self.char_to_idx = {c: i for i, c in enumerate(self.vocab)}
        self.idx_to_char = {i: c for i, c in enumerate(self.vocab)}
        self.text = [self.char_to_idx[c] for c in text]

    def __len__(self):
        return len(self.text) - self.seq_length

    def __getitem__(self, idx):
        x = torch.tensor(self.text[idx:idx+self.seq_length], dtype=torch.long).to(self.device)
        y = torch.tensor(self.text[idx+1:idx+self.seq_length+1], dtype=torch.long).to(self.device)
        return x, y

def train(model, optimizer, criterion, train_loader, num_epochs):
    model.train()
    for epoch in range(num_epochs):
        running_loss = 0.0
        hidden = model.init_hidden(train_loader.batch_size)
        for i, (x, y) in enumerate(train_loader):
            optimizer.zero_grad()
            output, hidden = model(x, hidden)
            loss = criterion(output.view(-1, output.size(2)), y.view(-1))
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
            if i % 100 == 99:
                print('[%d, %5d] loss: %.3f' %
                      (epoch + 1, i + 1, running_loss / 100))
                running_loss = 0.0
        print('Finished epoch %d' % (epoch + 1))

使用上述代码可以定义一个ChitGPT模型，并且训练该模型。需要注意的是，这里的ChitGPT模型是基于LSTM实现的。在训练过程中，需要提供一个ChitGPTDataset数据集对象，并将其作为参数传递给DataLoader用于生成批次数据。